integrated assessment of future cap policies: land...

Author's personal copy

Integrated assessment of future CAP policies:land use changes, spatial patterns and targeting

Annette Piorr a,*, Fabrizio Ungaro b, Arianna Ciancaglini c, Kathrin Happe d,Amanda Sahrbacher d, Claudia Sattler a, Sandra Uthes a, Peter Zander a

a Leibniz-Centre for Agricultural Landscape Research (ZALF), Institute of Socio-Economics, Eberswalder Straße 84,

15374 Muncheberg, GermanybResearch Institute for Hydrogeological Protection National Research Council (IRPI-CNR) Via Madonna del Piano 10,

50019 Sesto Fiorentino, ItalycUniversity of Florence, Department of Agricultural and Land Economy (DEART-UniFI) Piazzale delle Cascine 18, 50144 Firenze, Italyd Leibniz-Institute of Agricultural Development in Central and Eastern Europe (IAMO), Theodor-Lieser-Street 2, 06120, Halle (Saale), Germany

1. Introduction

The Common Agricultural Policy (CAP) reform 2003 has

aimed at stimulating global markets competitiveness, better

environmental performance, supporting rural viability as

well as better meeting consumer demands. From 2013

further far-reaching policy changes are expected leading to

on-going adaptation processes of European farms that will

change the rural landscapes and their socio-economic

conditions drastically. The FP6 EU-project MEA-Scope

(Micro-economic instruments for impact assessment of

multifunctional agriculture to implement the Model of

European Agriculture) carried out an ex-ante assessment

of scenarios on possible future developments of the CAP.

Special attention is paid towards specific rural development

potentials.

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 2 ( 2 0 0 9 ) 1 1 2 2 – 1 1 3 6

a r t i c l e i n f o

Published on line 27 February 2009

Keywords:

MEA-Scope

AgriPoliS

MODAM

Structural change

Natura 2000

Semivariance analysis

a b s t r a c t

The recent and upcoming reforms of the Common Agricultural Policies (CAPs) aim at

strengthening the multifunctional role of agriculture, acknowledging the differences in

economic, environmental and social potentials within European regions. This paper pre-

sents results from an integrated assessment of existing and future policies within the

framework set up in the FP6 EU project MEA-Scope. Spatial explicit procedures allow for the

MEA-Scope modelling tools to provide information related to regional, environmental and

socio-economics settings. The impact of different policy scenarios on structural change,

land abandonment and cropping pattern of typical farms has been assessed based on linked

agent-based (ABM) and Linear Programming (LP) models at regional and farm scale for two

study areas. For the German case study area Ostprignitz-Ruppin (OPR), the issue of policy

targeting has been addressed by relating non-commodity outputs (NCOs) to soil quality and

protection status. For the Italian case study area (Mugello), changes in landscape patterns in

terms of increased fragmentation or homogeneity as affected by changes in agricultural

intensity have been analysed using semivariance analysis. The spatial explicit approach

highlighted the relevance of case study research in order to identifying response structures

and explaining policy implementation patterns.

# 2009 Elsevier Ltd. All rights reserved.

* Corresponding author. Tel.: +49 33432 82222.E-mail address: [email protected] (A. Piorr).

avai lable at www.sc iencedi rec t .com

journal homepage: www.elsevier.com/locate/envsci

1462-9011/$ – see front matter # 2009 Elsevier Ltd. All rights reserved.doi:10.1016/j.envsci.2009.01.001


Within the establishment of rural development policies

and in accordance with sustainability goals, the European

Commission adopted formal impact assessment procedures

for ex-ante policy assessment (Tscherning et al., 2008).

Its core is related to policy driven land use changes and is

mainly concerned with environmental issues mostly

addressed at a pan-European scale (Lambin and Geist, 2006;

Helming et al., 2008; Rienks, 2008). In contrast, this paper

focuses on the regional and farm scale by adopting a

hierarchical model-based approach, which can explicitly

handle the complexity and dynamic change of farms and

structures. Moreover, it accommodates management inten-

sities and the variability of site conditions.

The presented policy impact assessment towards multi-

functionality made use of an indicator framework that

translated the function related concept of multifunctionality

to the concept of non-commodity outputs (Piorr et al., 2006,

2007a; Waarts, 2007). To cover the socio-economic dimension,

indicators such as farm size, farm income, livestock densities

and labour force input have been analysed. To represent the

environmental dimension, various abiotic and biotic indica-

tors have been chosen.

The regional level has been chosen in order to allow for

the analysis of mutual interdependencies of causal chains

and of structural change being observed at regional scale.

Thereby typical processes of policy implementation and

farming practice adaptation are examined. For example

different farms of initially different types in different

environmental settings develop different strategies of

adaptation to new compulsory guidelines and regulations

and to incentives set by voluntary measures as agri-

environmental programmes.

2. Methodology

2.1. The MEA-Scope modelling approach

The applied modelling approach is based on farm-level

models, which are loosely coupled in a hierarchical order

(Happe et al., 2006a,b; Damgaard et al., 2006) (Fig. 1).

AgriPoliS (Happe et al., 2008, 2006a,b) is an agent-based

(ABM) and dynamic model that simulates structural change

based on the individual actions and interactions between

large numbers of individual farms. The model takes account

of simultaneous decisions on production, labour, capital,

land allocation, and investments. MODAM (Zander and

Kachele, 1999) is a linear-programming (LP) model that

simulates cropping and livestock patterns of farms, which

are the basis for a fuzzy-logic-based environmental impact

assessment. It makes use of expert-knowledge that is

processed with the help of fuzzy-logic and results in

Indexes of Goal Attainment (IGA) which are expressed as

dimensionless indexes ranging from zero to one (Sattler

et al., 2006; Sattler, 2008). Farms in both models apply a

profit-maximization strategy on which decisions are based.

The results presented in this paper refer to two case study

regions: Ostprignitz-Ruppin (OPR) (Germany) and Mugello

(Italy). The specific advantage of MEA-Scope lies in its dynamic

perspective. In this light, MEA-Scope considers the dynamic

interactions of farms on the local land and product markets as

well as in simultaneous decision making on factor allocation

(Happe et al., 2006a,b; Osuch et al., 2007). For example, when

deciding on renting-in additional land, farms simultaneously

take into account a change in the farm’s production structure

or the allocation of labour, as well as off-farm activities. The

Fig. 1 – MEA-Scope modelling approach (www.mea-scope.eu).

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 2 ( 2 0 0 9 ) 1 1 2 2 – 1 1 3 6 1123


simulation results considered in this paper refer to the four

policy scenarios listed in Table 1. All policy scenario runs, start

from the Agenda 2000 situation (BAS00) and cover a time span

of 10 periods. A switch to the other policies takes place after 4

periods. The presented simulation results reflect medium

term developments—as simulated for period 9, which is 5

years after the implementation of the new policy options.

The reference scenario (REF) has been set up as an idealized

decoupled single farm payment, based on historical payments

3 years prior to policy change. The payment is completely

decoupled from land and is granted to the farm operator. It is

conditional on further running the farm and keeping land in

good agricultural condition (GAEC). The payment is not

distributed anymore to farmers who quit farming and is not

tradable, differing to that extent from a BOND scheme as

suggested by Swinbank and Tangermann (2001). As the

payment is not linked to land anymore, it can not be fully

compared to single farm payment schemes implemented in

some EU Member States as well. For second pillar measures

minimum grassland care was implemented as a payment of

130 s/ha and 200 s/ha respectively for OPR and Mugello. For

Natura 2000 areas the simulation by MODAM applied the

cross-compliance obligation of minimum care with a payment

of 200 s/ha and 450 s/ha respectively for OPR and Mugello.

The scenarios S01 and S02 refer to conditions in absence of any

direct payments.

In terms of grassland management practice, minimum care

means one mulching cut per year for grassland maintenance

without use for fodder production (GAEC obligation in Natura

2000 areas), while extensive grassland management means

low intensity management for fodder production (low live-

stock densities, low fertilizer input). In the following text the

term ‘‘land abandonment’’ refers to the decrease of the total

utilized agricultural area (UAA) in the region within the

observed time steps, e.g. if farms cease to operate but the land

is not taken over by other farms. The term ‘‘idle land’’ refers to

land which is not in use, and does not receive any payments,

but still belongs to an active farm.

For the localization of the farms in the case study region, a

spatial distribution approach was chosen that allows for a

spatial explicit analysis of structural changes and their

impacts on multifunctionality (Kjeldsen et al., 2006; Damgaard

et al., 2007; Ungaro et al., in press). Different modelling

strategies have been implemented for the different regions in

order to take regional peculiarities and data availability into

account. Further applications of the approach involve, i.e.

Happe et al., 2006a,b; Uthes et al., 2008; Damgaard, 2008.

2.2. Case study area descriptions and analytical approach

The German case study region Ostprignitz-Ruppin (OPR)

covers a utilized agricultural area (UAA) of about 125,000 ha

and is situated in North-Eastern Germany (Fig. 2). The region is

comparatively rich in extensive grassland, forests and wood-

land. The overall landscape structure is versatile including

water bodies, heath land and swamp areas. In 2003, the region

counted 585 farms with an average farms size of 200 ha and an

average livestock density of 0.5 LU/ha. There is a broad range

of farm sizes and cropping conditions, though yield expecta-

tion and soil productivity are comparably low on average. The

cropping pattern is dominated by winter rye, winter wheat,

maize, and rape production. 30% of the UAA is designated as

Natura 2000 areas.

The model is initialized with a set of 585 individual farms of

different initial types. They have been derived from farm

accountancy data of the European Farm Accountancy Data

Network (FADN). The database set up for the modelling

procedure comprises simulations on 1246 production prac-

tices for 35 different crops in 2 intensities for 6 site qualities

and taking into account 7 livestock branches. For the analysis

of impact assessment results, farms in the sample were

grouped in sub samples representing two target groups of the

policies. Target groups were identified based on farm

specialization and the share of land in protected areas. The

selection criteria were protection status of the site (Natura

2000), and specific site conditions (soil quality class), which

Table 1 – The MEA-Scope policy scenarios; results are always considered at year 0 (BAS00 = initial state) and at year 9(medium term). AEP: agri-environmental programme. GAEC: good agricultural and environmental condition.

Scenario First pillar Second pillar

BAS Agenda 2000 - Full implementation of Agenda 2000 at the

end of 2002

AEP - Agri-environmental

programme on extensive

grazing land- No cross-compliance Natura 2000

REF Reference - Idealized decoupled single farm payment (SFP) AEP - Agri-environmental


grazing land

- Historical payment (3 year average) paid to the

farm operator

Natura 2000

- Conditional on running the farm

- Cross-compliance: GAEC minimum care

(all farmland has to be kept in good agricultural

condition (at least cutting once a year))

S01 Liberalization +

Environment

- Removal of direct payments AEP - Agri-environmental


grazing land

- Cross-compliance: GAEC minimum care

(all farmland has to be kept in good agricultural

condition (at least cutting once a year))

Natura 2000

S02 Liberalization - Removal of direct payments No AEP

- No Agri-environmental programme No Natura 2000

- No cross-compliance



were translated into rules and thresholds for the analysis of

the two target groups (Table 2).

The extremely heterogeneous Mugello territory (elevation

160–1241 m a.s.l.) covers a UAA of about 26,000 ha in Central

Italy (Tuscany) and is characterized by small mixed crop-

livestock farms (total number 1237, average farm size 22 ha),

mostly engaged in the total cow-calf line mixed farming. The

beef sector is made of traditional farms with forage crops or

grassland for grazing. Livestock density is 0.3 LU/ha. Mountain

pastures and permanent grasslands dominate the land-use,

followed by fodder crops such as alfalfa and forage sorghum.

Important arable crops are grain maize, barley and durum

wheat. The model was initialized with 1237 farms of different

types, which have been derived from FADN data. The database

set up for the modelling procedure comprises simulations on

188 production practices for 25 different crops in 2 intensities

for 12 site types and taking into account 4 livestock branches.

The extreme heterogeneity of the Italian study region

claimed for a spatially explicit assignment: based on produc-

tion techniques farms were allocated in specific ‘‘field types’’

based on a multi-criterion approach in a GIS-environment

(Ungaro et al., 2006, in press) (Fig. 3). A farmstead ID number

identified each individual farm allocated randomly in the

region. Each field type (Table 3) resulted from a combination of

soil capability class, terrain morphology, and elevation class

and is characterized by different intensity of land use. In each

field type, given a specific set of environmental constraints the

typical crop rotations and associated production techniques

were allocated. Crop allocation and associated production

techniques resulted from direct surveys and interviews,

statistical data from the agricultural census (ISTAT, 2002)

and revised Corine Land Cover (APAT, 2004).

The effects of the changes in land use intensity on the

environmental indicators can be analysed considering an

indicator directly related to crop management practices such

as risk of nitrate leaching or risk of soil loss due to water

erosion. Each production system is characterized by a certain

level of inputs and field operations which determine land use

intensity at a given site, i.e. at 1 ha plot level, providing the

basis for the environmental impact assessment (EIA) within

MODAM (Sattler et al., 2006; Sattler, 2008). Since MODAM-EIA

results for a given area reflect the underlying production

system(s) and the specific crop rotations associated with it,

then the spatial pattern of a selected indicator reflects the

Fig. 2 – Case study area, land cover type map and site type map Ostprignitz-Ruppin (OPR) (Germany).

Table 2 – Selection criteria for the analysis of target farmgroups for agricultural development targets for the OPR(DE) study area.

Development target environment

E–G: ‘‘Extensive grassland farms’’

� >80% of the farm UAA in Natura 2000 and

� >40% of the farm UAA on low productivity grassland

E–A: ‘‘Arable farms’’

� >80% of the farm UAA in Natura 2000 and

� >40% of the farm UAA in soil quality class 25

Development target competitiveness

C–A: ‘‘Arable farms’’

� 100% of the farm UAA in soil quality class 38

� Only farms remaining in production in the liberalization

scenario on the medium term

C–G: ‘‘Intensive grassland farms’’

� >40% of the farm UAA on intensive grassland

� No extensive grassland

� Only farms remaining in production in the liberalization

scenario on the medium term



patterns of the different crops combinations in terms of

agricultural intensity. Its variations over time under the

different scenarios are due to correspondent variations in

cropping patterns. Such variations in agricultural intensities at

landscape scale can be analysed in a spatially explicit context

resorting to semivariograms calculation and modelling (Ernoult

et al., 2003). Variograms are increasingly being used to

investigate spatial pattern of raster data providing information

about the spatial variability structure of the variable of interest,

including land use (Dendoncker et al., 2007).

3. Results

The analysis focuses on environmental and land use impacts

for different development targets. The reasoning behind

structural adjustments in terms of farm size, farm numbers,

and the allocation of labour is discussed in detail in Osuch

et al. (2007).

3.1. Land use change: development target environment

The maintenance of permanent grassland belongs to the

preferential environmental objectives of agriculture in the

region OPR and in the Federal State of Brandenburg in general.

Herewith, especially grassland in wetland areas, often

characterized by a comparably low productivity, should be

preserved due to biodiversity, habitat and landscape amenity

reasons. Amongst farms that are supposed to contribute the

most to environmental objectives, those located in Natura

2000 areas are of particular interest. The research question for

the CAP policy assessment is to analyse the environmental

impact of structural change and management shifts in

designated areas.

Fig. 3 – Case study area and field type map Mugello (Italy).

Table 3 – Field types classification for the Mugello (IT) study area.

Plant production system Site Soil land capability class

Use Altitude m a.s.l. Intensity Field type (share %) Morphology First Second Third

Arable Valley <300 High VL (7.7%) Plain (slope <5%) 3 3/4

VH (54.4%) Terraces (slope >5%) 2 3

Hills 300–700 Medium HL (28.9%) Low (<500 m) 3/4 4 4/6

HH (8.4%) High (>500 m) 4 6

Mountain >700 Low ML (0.6%) Low (<900 m) 6 4

Grassland Valley <300 Low VL-G (0.14%) Plain (slope <5%) 3 3/4

VH-G (50.7%) Terraces (slope >5%) 2 3

Hills 300–700 Low HL-G (21.2%) Low (<500 m) 3/4 4

HH-G (50.7%) High (>500 m) 4 6 4/6

Mountain >700 Low ML-G (21.2%) Low (<900 m) 6 4

MH-G (8.7%) High (<900 m) 6 4/6



Farms with a high share of extensive grassland and Natura

2000 area (E–G) show adaptation behaviour (Fig. 4a). The area

in use (initially 10,408 ha) undergoes an extreme reduction in

the Agenda 2000 (�76%) and idealized single farm payment

(REF) scenarios (�27%) coupled with an ever higher reduction

in the number of farms (�85% and �51%). Minimum care

already takes a high share of grassland use in the initial state.

Under both scenarios, green fodder production with 2 cuts is

completely given up and turned into minimum care. In the REF

scenario, this takes place despite the complete abolishment of

livestock husbandry (suckler cows). In the scenario without

direct payments (S01) there is a dramatic increase of

abandoned land (+97%) (Table 4). The reason is that minimum

grassland care is not practiced due to the lack of incentives for

this measure in the ‘‘liberalization’’ settings.

The specific analysis for arable farms within the target

group with high share of UAA in Natura 2000 areas (E–A)

resulted in the findings reported in Table 4. Changing the

support scheme to an idealized single farm payment (REF)

leads to a significant shift in land use away from E–A and E–G

farms towards farms with more market-oriented production

(C–A and C–G). The total area in use (initially a total UAA of

2358 ha) undergoes a pronounced reduction, which in the

Agenda 2000 and idealized single farm payment (REF)

scenarios is less extreme (�23% to �30%) than the reduction

in number of farms. In the single farm payment scenario 40%

of the arable farms on poor soils survive (Fig. 4b). Without

direct payments (S-scenarios) less than 15% of the UAA of the

region remains in agricultural use of arable farms. Farms of the

target group are selected as they use land as well inside as

outside of Natura 2000 areas what allows for distinguishing

policy responses accordingly. Natura 2000 areas underlie land

abandonment in a similar way as non-designated sites, but less

fertile soils are more likely to be abandoned than fertile soils

(Fig. 5). The preference for set aside leads to a reduction of

initially diverse and extensive arable cropping by 80% (Fig. 5a).

Especially the traditional winter rye production is given up.

Fig. 6 shows the environmental impacts of the land use change

compared to the initial situation and to the average of all farms

in OPR. The idealized single farm payment leads to a clear

improvement of all biotic and abiotic indicators (see also Uthes

et al., 2008). Only groundwater recharge potential is markedly

reduced due to the further extensification respectively abol-

ishment of grassland use. The overall average of OPR farms,

with less land abandonment but a diverse low input cropping

pattern prove the tendency to a better environmental perfor-

mance than the farms selected as target group environment

(Fig. 6a). The baseline scenario shows opposite results but a less

distinct improvement of the environmental situation (Fig. 6b).

3.2. Land use change: development target competitiveness

The region OPR is characterized by a comparably low

productivity in terms of soil fertility classes and yields on

Fig. 4 – Land abandonment related to site qualities in farms with high Natura 2000 share in response to idealized SFP

compared to Agenda 2000, simulated for year 9.

a. Total area in use in farms >40% of UAA extensive grassland, b. total area in use in farms >40% arable land in site class 25.

Table 4 – Absolute (in ha) and relative (difference in % to initial situation) changes in utilized agricultural area in responseto different policy scenarios after 9 years of implementation.

Scenario E–G E–A C–A C–G

BAS00 10.408 Change (%) 2.358 Change (%) 19.996 Change (%) 100 Change (%)

BAS09 2.494 �76 1.641 �30 22.601 13 188 88

REF09 6.723 �35 1.680 �29 20.186 1 146 46

S0109 0 �100 0 �100 6.409 �68 211 111

E–G (Environment–Grassland): farms >40% extensive grassland, >80% Natura 2000; E–A (Environment–Arable land): farms >40% arable land in

site class 25, >80% Natura 2000; C–A (Competitiveness–Arable land): farms with 100% arable land, soil class 38, remaining in liberalization

scenario; C–G (Competitiveness–Grassland): farms >40% grassland, only intensively managed, remaining in liberalization scenario.



both arable land (dominance of sandy soils) and grassland

(high share of wetlands). The MODAM modelling system

distinguishes soil quality classes based on a soil rating

index ranging from 7 to 100 (Stremme, 1951). In the case

study region OPR, most sites only belong to the classes I

(<25) and II (25–>38), with very low to medium fertility. For

the target group ‘‘competitiveness’’, only farms in class II

sites have been selected. The simulation results show

that the area in production remains stable under the

conditions of scenario REF (Table 4), with 92 farms of an

average size of 172 ha. The cropping pattern of those

comparatively ‘‘competitive’’ arable farms (C–A), shifts into

a pure cash crop rotation with low diversity (winter wheat,

winter rye and winter rape) (Fig. 5b). Formerly set aside area

is completely returned into production. If soil conditions do

not allow for extension of cash crop area, land is rather

abandoned. Mixed farms keep dairy cows numbers

unchanged and use grassland at relatively high intensity.

Fig. 5 – Change in cropping pattern in response to policy scenarios: (a) farms at low soil quality sites with high Natura 2000

share and (b) farms at medium soil quality sites.

Fig. 6 – Index of Goal Achievement (IGA) performance relative to initial state: comparison of farms in the E–A target group

and average of all farms in the German case study region OPR, (a) Agenda 2000 (BAS), (b) idealized decoupled single farm

payment (REF).



Without direct payments however the area in production is

reduced markedly (�68%) (Table 4). At the same time the

number of farms shows a drastic reduction. In the S01

scenario, only 25% of the farms remain active in the

medium term.

3.3. Land use change: spatial patterns related to field type

As land use is one of the primary determinants of ecosystem

vulnerability, the assessment of changes in land use pattern

for the different scenarios is crucial to understand how the

different policy scenarios affect environmental services

provided by agriculture. Land use patterns nearly always

exhibit a certain degree of spatial autocorrelation, resulting

from the interaction between landscape features and gradi-

ents and farming technologies (Verburg et al., 2006). Spatial

autocorrelation, measuring the level of interdependence

between variables, provide a mean to elucidate and describe

spatial patterns in the landscape. The changes in autocorrela-

tion can be then detected, modelled and used to support the

analysis of CAP policy scenarios.

The land use share (% total UAA) for the whole Italian case

study area at the initial reference state (BAS00) is illustrated by

Table 7. This general cropping pattern is highly differentiated

in terms of occurrence of the different crops in the different

field types (Table 5).

Grassland in Mugello is exclusively run under extensive

grassland use, i.e. grassland areas (3–10 years) that receive

minimum grassland care of one cut per year. Common to all

scenarios is an increase of arable land which is coupled with

an almost complete or complete (under S0209) disappearance

of grasslands. The initial grassland share of 47% is reduced to

29%, 31% and 6% respectively for the Agenda 2000 (BAS09), the

idealized single farm payment (REF09) and the partial ‘‘liberal-

ization’’ (S0109) scenario. Under all scenarios there is a

dramatic abandonment of the mountain grassland field types

(MH-G and ML-G): �62% at BAS09, �51% at REF09 and �91% at

S0109.

The change in the share of set aside land provides a clear

picture of the structural changes under decoupled subsidies

that result in a relevant increase of uncultivated land (from

2215–5951 ha). Under the condition of ‘‘no subsidies’’ (i.e.

under the S01 and S02 scenarios) on the contrary, there is a

(nearly) complete disappearance of set aside land and an

increase in cultivated areas. In hilly field types (HH and HL) the

structural changes under decoupled subsidies lead to an

increase of arable lands and at the same time result in a

relevant decrease of cultivated areas. In the valley field types

(VH and VL) the structural changes under absence of subsidies

result in an increase of arable lands and at the same time lead

to the abandonment of set aside practices with a relevant

increase of cultivated areas under cereals, with maize and

barley dominating the more productive valley field types.

Typical crops such as alfalfa and spelt take over set aside and

fava bean in the less productive hilly field types.

3.4. Land use change: spatial pattern related toagricultural intensity

In order to assess how agricultural landscape patterns are

affected by policy driven land use change, an index of goal

attainment (IGA) was calculated for various indicators and

accounted environmental goals such as the reduction of soil

water erosion risks. The closer the index is to 1 the lower the

land use related risks are assumed. MODAM does not

simulate single crops but crop rotations within a given

Table 5 – Crop share in the arable field types for the main crops of the area: relative differences (%) at year 9 with respect toinitial state; in italics crops whose share drops down to 0%. HH: high hills field type; HL: low hills field type; VL: valleyplain field type; VH: valley terraces field type.

Scenario Field type Set aside Maize Barley Durum Spelt Alfalfa Fava bean

Initial HH 26.3 0.0 14.9 0.0 16.4 9.0 26.3

BAS09 HH 2.4 0.0 12.5 0.0 2.6 2.1 �15.6

REF09 HH 27.5 0.0 5.3 0.0 �0.5 �0.1 �26.3S0109 HH �18.9 0.0 14.0 0.0 24.8 13.4 �26.3S0209 HH �26.3 0.0 10.5 0.0 37.2 11.9 �26.3

Initial HL 26.8 0.0 15.9 14.0 13.3 8.2 17.4

BAS09 HL �2.6 0.0 9.9 �5.1 4.3 2.4 �7.3

REF09 HL 29.5 0.0 2.9 �14.0 1.9 0.7 �17.4S0109 HL �18.5 0.0 10.1 �14.0 30.7 13.6 �17.4S0209 HL �26.8 0.0 11.9 �14.0 36.5 14.2 �17.4

Initial VH 10.9 24.7 27.6 17.0 0.0 9.0 10.7

BAS09 VH �3.9 1.0 3.0 �0.1 0.0 0.5 �0.7

REF09 VH 23.2 �11.1 0.7 �17.0 0.0 �0.3 �10.7S0109 VH �10.9 24.7 0.3 �17.0 0.0 0.8 �10.7S0209 VH �10.9 29.3 �3.2 �17.0 0.0 0.5 �10.7

Initial VL 2.8 24.2 25.9 27.0 0.0 10.2 9.2

BAS09 VL 2.6 1.2 �0.9 �1.5 0.0 0.8 �1.9

REF09 VL 32.2 �10.5 1.2 �27.0 0.0 �1.5 �9.2S0109 VL �2.8 17.3 9.7 �27.0 0.0 �0.4 �9.2S0209 VL �2.8 20.2 6.7 �27.0 0.0 �0.4 �9.2



production system since usually more than one crop is

allotted to one plot. Hence, it is not possible to localize a single

specific crop in each 1 ha plot at the different time steps but

only the share of each single crop and hence the assessment

in terms of IGA, is averaged for the whole plot. For this specific

indicator its spatial pattern reflects then the patterns of the

different crops combinations in terms of soil protection and

input intensity, and its variations over time under the

different scenarios are due to correspondent variations in

cropping patterns.

Fig. 7 – Case study Mugello, IGA Water Erosion: spatial distribution at initial state and at year 9 under the different policy

scenarios.



In order to evaluate the different crop production practices

regarding their potential to contribute to water erosion, the

following parameters are considered in MODAM:

� the degree of soil-coverage,

� the cultivation method (i.e. zero/no tillage, under-sowing,

catch crops, sod seeding etc.),

� soil compaction in the winter half year (due to machinery

passages and field operations).

All the production practices of all crops defined for the

model have been rated by their Index of Goal Attainment (IGA)

using a fuzzy tool with the optimum (IGA WaEro = 1) being a

zero risk of soil erosion. Best rated among those evaluated are

set aside, grassland and alfalfa. Row crops like potatoes,

maize, and sunflowers are given the lower ratings while the

intermediate score goes to cereals depending on the time of

seeding.

For the Mugello region, the average IGA for water erosion

risk (IGA WaEro) exhibits a dramatic decrease under the two

scenarios without direct payments (�31 and �35% respec-

tively under S01 and S02 with respect to the initial state), a

relevant increase under the idealized single farm payment

scenario (+13%) and a weak decrease under Agenda 2000

(�2%). Nevertheless these overall figures are quite differen-

tiated in the different field types, with the hilly field types

exhibiting a positive trend under all the policy scenarios and

the valley field type exhibiting opposite trends. For the VH field

type the trend is positive under REF (+27%), negative under BAS

(�3%), S01 (�32%) and S02 (�33%), while for the VL field type

the trend is positive under REF (+29%) and BAS (+7%), negative

under S01 and S02 (�17%). These trends are made clear in the

raster maps in Fig. 7 (pixel size 1 ha) which show the spatial

distribution of the IGA for risk of soil erosion at the initial state

and at year 9 under the different policy scenarios. The

differences in the spatial patterns of the indicator (Fig. 7)

result from:

� reduction or disappearance of grasslands in parts of the

area,

� changes in set aside land under the different scenarios,

� changes in land use intensities related to different crop

patterns.

In order to quantify such differences in the spatial

distribution of the IGA for water erosion, the standardized

semivariograms for this indicator at year 9 under the different

policy scenarios were calculated and interpolated with

authorized models (Goovaerts, 1997). The semivariogram is

a function describing the degree of spatial dependence of a

variable Z(x) which is assumed to result from a stochastic

process. The semivariogram g(h) is computed as half the

expected squared increment of the values between locations x

and x + h:

gðhÞ ¼ 12n

Xn

i¼1

fZðxÞ � Zðxþ hÞg2

where n is the number of pairs of sample points separated by

the distance h; g(h) is calculated for all possible lag distance

classes in a data set. It is commonly represented as a graph

showing the semivariance as function of increasing distance

between all pairs of sampled locations. Such a graph is helpful

to build a mathematical model that describes the variability of

the measure with location. The omnidirectional standardized

semivariograms and their models are shown in Fig. 8. In all

cases a double nested spherical model proved to provide the

most suitable model; Table 6 shows the parameter for the

model used to interpolate the semivariograms.

The changes in the values of the structural component of the

variograms result from substantial changes in land use pattern,

with clear and constant modifications with respect to the initial

state. This trend is characterized by an increase of the nugget

effect C0 (i.e. the spatially uncorrelated variance), whose ratio to

the total sill (i.e. the spatially correlated variance) increases

under all scenarios with respect to the initial state. This is

coupled with a decrease of the ranges of the variograms (i.e. the

distance at which the observations are no longer spatially

correlated), particularly evident for the long range component

A2 of the nested model. The increasing spatially uncorrelated

variance suggests an increase of the spatial randomness. This

implies a decrease of spatially structured variability, and a

higher degree of fragmentation. The reduction of the range

indicates a decrease in the size of patches with similar land use

intensity, which under the scenarios without direct payments is

more likely to be surrounded by smaller patches of contrasting

land use intensity with respect to the other scenarios and to the

initial state.

Table 6 – Standardized omnidirectional semivariograms for IGA WaEro at time 9. Sph.: spherical model = 1.5 T {[(distance/range)] S 0.5 T [(distance/range)3]}.

Scenario Nugget C0 Model 1 Sill C1 Range A1, m Model 2 Sill C2 Range A2, m Total sill Nugget/sill

BAS00 0.12 Sph. 0.23 1650 Sph. 0.60 11000 0.83 0.14

BAS09 0.18 Sph. 0.29 2530 Sph. 0.53 9680 0.82 0.22

REF09 0.19 Sph. 0.24 2640 Sph. 0.59 9680 0.83 0.23

S0109 0.48 Sph. 0.37 1440 Sph. 0.12 6000 0.49 0.98

S0209 0.59 Sph. 0.32 1560 Sph. 0.12 5132 0.44 1.34

Table 7 – The land use share (% total UAA) for the wholeItalian case study area at the initial reference state(BAS00).

Land use Percentage

Barley (Hordeum vulgare) 23

Set aside 17

Durum wheat (Triticum durum) 16

Maize (Zea mays) 15

Field bean (Vicia faba var. minor) 14

Alfalfa (Medicago sativa) 9

Spelt (Triticum spelta) 5

Sunflower (Heliantus annus) 1



4. Discussion

4.1. Policy implications: targeting of policies

Targeting, understood as appropriate objective setting and

instrument provision, has so far been mainly directed at

geophysical conditions, and at the limitation of environmental

threats to sensitive areas. A spatially explicit approach is

required in order to properly evaluate the impacts of the

different scenarios on the environmental services provided by

agriculture. This approach will equally provide sound indica-

tions to policy makers and stakeholders. Different from other

policy impact assessment tools that work on highly aggre-

gated scales, the hierarchically linked MEA-Scope modelling

approach connects spatially explicit analysis with actions and

interactions of individual farms. As regards policy impact

assessment, the resulting aggregate effects are thus the

results of individual adjustment reactions to the policy at

Fig. 8 – Experimental standardized semivariograms (ominidirectional) and semivariogram models for IGA Water Erosion at

initial state and at year 9 under the different policy scenarios. Black dots: experimental semivariogram; continuous line:

semivariogram model.



the farm level given specific spatial characteristics. Results

can thus be, linked to specific target groups of farms or to

spatial characteristics that may correspond with development

targets. The first chosen approach to do so is a criteria and rule

based selection of farm groups that are identified as the typical

implementers of environment related strategies and mea-

sures, respectively related to the development target competi-

tiveness.

Results from the application of the MEA-Scope approach to

two different regional settings indicated that for extensive

sites the shares of land abandonment was highest in any kind

of policy configuration. The maintenance of permanent

grassland on less productive sites, mainly wetlands in the

OPR landscape, belongs to the main environmental objectives

of the region. Especially grassland farms on those sites turned

out to quit farming to a significantly higher share than the

average farm in OPR. While differing in magnitude between

policy scenarios, the general direction of this effect cannot be

reversed by providing additional incentives to use grassland as

part of an agri-environmental scheme. While under Agenda

2000 and without direct payments especially those low

productive grassland sites are abandoned drastically, the

idealized single farm payment allows for a certain buffering of

the reduction. Mixed farms undergo a less dramatic structural

change, as they make use of the set aside option on arable land

and the minimum care measure on grassland. As regards the

latter, it is applied whenever support payments to farms are

decoupled from keeping livestock on grassland. In this case,

grassland use is decoupled completely from fodder produc-

tion, and thus has the character of a pure NCO production. Yet,

as soon as payments are phased out, farms will abandon the

land completely due to a lack of incentive. Interesting is the

finding that land abandonment on low fertility sites takes

place slightly more in non Natura 2000 when compared with

other sites. To a certain degree this means payments related to

Natura 2000 as an obligation in the idealized single farm

payment scenario save land from abandonment, especially

low quality grassland. These results strengthen support

towards targeting measures as far as the maintenance of

low productive grassland in use is the objective. With regard to

the environmental impacts, the outcomes of the simulations

are less distinct from the average than expected. Though the

single farm payment results in a clear improvement of most

environmental indicators, farms with a high share of area

located in Natura 2000 sites show no better environmental

performance than the average low intensity farming of the

region. Particularly for arable farming, the value of the set

aside measure (connected with payments on arable land

without yielding a crop) implemented at such a high share is

more than questionable in terms of cultural landscape

preservation and identity. Traditional winter rye production

and grassland use are turned into set aside and minimum

care. Farmers apply a profit maximization strategy with full

exploitation of agri-environmental payments on poor soils as

far as possible if it is profitable. If not, land is left idle. In order

to reach a higher added value of arable sites with Natura 2000

designation, clearer restrictions should be set, in terms of

maintenance of a certain diversity within crop rotation, as

they e.g. inherently exist within the system of organic

farming.

The site class under consideration in the target group

competitiveness (C–A), although constituting the ‘‘high’’

quality soils of the region OPR, in general terms only posses

an average potential yield capacity. Accordingly, the low share

of arable land kept in production and able to compete in

absence of direct payments (S-scenario) is a consequence of

insufficient yield potentials due medium soil fertility condi-

tions. The highly above average results for the intensive

grassland farms in OPR that markedly increase area, have to be

interpreted from this background too, as they are a result of

the comparatively low competitive capacity of the arable

farms with regard to cash crop production. The plots become

interesting for fodder cropping according to the comparatively

lower value on the land market, resulting finally in a shift

towards bigger farms with animal husbandry in general and

hence a shift in farm type distribution. Cropping pattern

results support this development. The cropping pattern of

arable farms on medium/good soils is characterized by low

diversity and cash crop orientation. The more the policy

presses towards market-orientation, the less different crops

are cultivated. Under the condition of profit maximization, if

soil conditions do not allow for extension of cash crop area,

land is rather abandoned than used for less attractive crops.

Amongst other animal husbandry sectors, only dairy cows are

kept in production if payments are phased out (Piorr et al.,

2007b). The policy settings for farms that turn out to work

competitively and successfully throughout all years, are

targeted in so far as the single farm payment can be assessed

clearly advantageous compared to the Agenda 2000 scenario.

4.2. Policies implications: land use patterns

The outcome for the Mugello scenarios in terms of land use

controlled environmental services can be distinguished in two

groups. Idealized decoupled single farm payment (REF) results

in an extensification of the region land use (increase of arable

lands in hilly field types, relevant decrease of cultivated areas,

increase of typical crops such as alfalfa and spelt replacing

cereals). Phasing out of direct payments (S01, S02) results in an

intensification of the regions’ land use (increase of arable

lands in valley field types, abandonment of set aside practices

and increase of cultivated cereal such as maize and barley).

All scenarios show an evolutionary trend characterized by

the disappearance of open areas, which is complete under the

scenarios without direct payments. This is coherent with the

historical data for the mountainous areas of the central

Apennine (�18.6% between 1990 and 2000) (ISTAT, 2002).

The indicator of soil erosion is highly affected by policy

induced land use changes and crop shares. An idealized

decoupled single farm payment with AEP results in a marked

reduction of average soil erosion risk with respect to the

reference situation, while phasing out of direct payments

leads to a marked increase in soil erosion risk with respect to

the initial state. The effects on erosion risk are highly

differentiated across the landscape, with the soils of the

valley terraces, characterized by a higher capability and

productivity, to be considered the most vulnerable to policy

driven changes. On the other side, all scenarios result in

reduced erosion losses from the lower capability hilly field

types, where the losses of set aside under scenarios (without



direct payments) are counterbalanced by an increase of

alfalfa.

The changes in land use intensity highlighted by the

different spatial autocorrelation functions observed for the

different scenarios indicate that the scenarios induced land

use changes at medium term are likely to results in radical

changes of landscape patterns. The indicator of soil erosion

risk assumed here as indicator of land use intensity suggests a

strong homogenization with low correlation under the two

scenarios with phased out payments as opposed to a complex

and well structured pattern with a high degree of spatial

dependence under single farm payments and Agenda 2000.

For the scenarios without direct payments the spatially

uncorrelated variance C0 is >50% and the range of auto-

correlation A is dramatically reduced (short range continuity),

indicating an increasing homogeneity (randomness) in the

land use intensity patterns. These, with respect to the initial

state, appear to be characterized by progressively smaller and

weakly aggregated spatial structures characterized by a higher

degree of fragmentation.

5. Conclusions

The demand on ex-ante policy impact assessment to assist the

knowledge creation process among decision makers is

increasing. Especially the fundamental changes of the CAP

accompanied by increased accountability requirements for

policy makers underline that more specific knowledge on

policy implementation is essential. This applies as well to

policies and measures targeted on specific objectives such as

the provision of public benefits, as to responses of farms

related to their specific socioeconomic and geophysical

framework conditions. Simulation models that work at highly

disaggregated scale, combining the farm level and regional

scale, may facilitate the development of knowledge on

potential adjustment reactions and their impact. Neverthe-

less, the results presented in this paper have to be qualified

from the point of view that decisions was made based on

economic concerns. All three models applied in the modelling

cascade run the simulations from the assumption of profit

maximization being the main driver for decisions of change.

Whereas there may be some grounds for this assumption,

with regard to a better targeting of policies promoting the

multiple functions of agriculture other behavioural drivers

should be taken into account. Hence, farmer activities are also

related to their individual value canon. They are determined

by the environment and the resulting perception of needs as

well as social factors (e.g. imitation, normative influences,

comparison processes). Further research should therefore

integrate such elements, also in enlarging modelling capabil-

ities.

The tool approach used provides insights regarding the

distributional effects among single farm agents, showing their

individual development over time as result of differences in

resource endowments (labour, capital, and land), and the

farms’ land market and investment activities. Because each

farm agent is spatially localized in a regional area, the

approach is capable of addressing policy induced farm

adaptation strategies, and spatially explicit impacts resulting

from on-farm structural change and changes in cropping

pattern or management practices.

The results from the application of the approach to

Ostprignitz-Ruppin, Germany and Mugello, Italy show that

the impacts of the simulated CAP scenarios differ across

different soil and climate characteristics, a result of different

site and farm type specific management decisions that go

from changes in cropping pattern to complete abandonment

of production branches (husbandry) or of the whole farm.

Farms located at rather favourable site conditions were the

beneficiaries of the idealized single farm payment scenario

(REF) while in marginal areas, particularly in NATURA 2000

areas, a high share was turned into set aside land. Without

direct payments (S02 scenario) arable farming in these areas

could not be maintained at all. Moreover, give the simulation

results support that on favourable site conditions, the

acceptance of extensification measures, e.g. set aside or

minimum care on grassland, is lower. Such an increased

abandonment of arable land connected with a loss of diversity

and habitats would run contrary to the objectives of NATURA

2000 area designated for the purpose of environmental

protection and preservation of traditional landscapes.

The spatially explicit hierarchical analysis applied to the

Italian case study region Mugello allowed for the analysis of

the interdependencies of policy driven land use changes and

impacts on the landscape pattern. The phasing out of direct

payments (S02) leads to distinct structural changes resulting

in a more homogeneous cropping pattern. An originally

diverse landscape developed towards a more uniform one

not only from the visual diversity value but also with regards

to biodiversity and environmental impacts.

Both case studies show that site-specific settings within

regions have different and marked impacts on the particular

policy adaptation strategies of farms, and that this results in

clearly different landscape patterns. The results underline the

necessity to take the heterogeneity of regions and farm

structures into account for ex-ante policy assessment,

especially if implementation impacts on landscape fragmen-

tation are considered.

Acknowledgements

This work was carried out as part of the EU funded 6th

framework project MEA-Scope (Micro-economic instruments

for impact assessment of multifunctional agriculture to

implement the Model of European Agriculture, SSPE-CT-

2004-501516). The authors wish to thank all the members in

the consortium who contributed to the discussion that helped

shape this work and Tim Hycenth Ndah for his helpful

assistance in revising the manuscript, as well as the

anonymous referees whose comments and suggestions

improved both clarity and precision of the paper.

r e f e r e n c e s

APAT, 2004. Corine Land Cover 2000 Toscana 1:100,000 Availablefrom: http://www.clc2000.sinanet.apat.it/ [Accessed 15November 2008].



Damgaard, M., Kjeldsen, C., Ungaro, F., Ciancaglini, A., Osuch,A., Happe, K., 2007. Spatial characteristics, scale and scope:operability with reference to spatial modelling. Series ofReports of the FP6 Research Project MEA-Scope, vol. 10.Muncheberg, Germany. Available from: http://project.zalf.de/meascope/documents/MEA-ScopeD7.2.pdf[Accessed 15 November 2008].

Damgaard, M., Osuch, A., Happe, K., Uthes, S., Reinhardt, F.-J.,Sattler, C., Zander, P., Detlefsen, N., Hutchings, N., 2006.Documentation of proto-types with special focus on theircapability to analyse multifunctionality policies. Series ofReports of the FP6 Research Project MEA-Scope, vol. 7.Muncheberg, Germany. Available from: http://project.zalf.de/meascope/documents/MEA-ScopeD3.4.pdf[Accessed 15 November 2008].

Damgaard, M., 2008. Multifunctional agriculture in a regional,spatial and dynamic context: an agent-basedrepresentation of policy response. Doctoral dissertation.University of Halle, 261 pp.

Dendoncker, N., Rounsevell, M., Bogaert, P., 2007. Spatialanalysis and modelling of land use distributions in Belgium.Computers, Environment and Urban Systems 31, 188–205.

Ernoult, A., Bureau, F., Poudevigne, I., 2003. Patterns oforganisation in changing landscapes: implications for themanagement of biodiversity. Landscape Ecology 18,239–251.

Goovaerts, P., 1997. Geostatistics for Natural ResourcesEvaluation. Oxford University Press, New York.

Happe, K., Kellermann, K., Balmann, A., 2006a. Agent-basedanalysis of agricultural policies: an illustration of theagricultural policy simulator agripolis, its adaptation andbehaviour. Ecology and Society, 11 (1), 49. http://www.ecologyandsociety.org/vol11/iss1/art49/.

Happe, K., Damgaard, M., Osuch, A., Sattler, C., Zander, P.,Uthes, S., Schuler, J., Piorr, A., 2006b. CAP-reform and theprovision of non-commodity outputs in Brandenburg.Agrarwirtschaft, German Journal of Agricultural Economics55 (5/6), 268–279.

Happe, K., Balmann, A., Kellermann, K., Sahrbacher, C., 2008.Does structure matter? The impact of switching theagricultural policy regime on farm structures. Journal ofEconomic Behaviour and Organization 67, 431–444.

Helming, K., Perez-Soba, M., Tabbush, P. (Eds.), 2008.Sustainability Impact Assessment of Land Use Changes.Springer-Verlag, Berlin.

ISTAT, 2002. 5 (Censimento generale dell’Agricoltura 2000.Available from: http://www.istat.it/censimenti/agricoltura/[Accessed 15 November 2008].

Kjeldsen, C., Dalgaard, T., Bøcher, P.K., 2006. Methodologicalissues of modelling farm and landscape indicators forsustainable land systems. Danish Journal of Geography 106(2), 35–43.

Lambin, E.F., Geist, H.J., 2006. Land-Use and Land-Cover Change.Local Processes and Global Impacts. Springer-Verlag, Berlin.

Osuch, A., Happe, K., Sattler, C., 2007. Analysis of exemplarypolicy issue: assessment of multifunctional impact ofagricultural joint-production vs. targeted payments/non-agricultural provision. Series of Reports of the FP6 ResearchProject MEA-Scope, vol. 12. Muncheberg, Germany.Available from: http://project.zalf.de/meascope/documents/MEA-ScopeD7.4.pdf [Accessed 15 November2008].

Piorr, A., Uthes, S., Waarts, Y., Sattler, C., Happe, K., Mueller, K.,2006. Making the multifunctionality concepts operationalfor impact assessment. In: Meyer, B.C. (Ed.) SustainableLand Use in Intensively Used Agricultural Regions.Landscape Europe. Alterra Report No. 1338, Wageningen,pp. 47–54.

Piorr, A., Muller, K., Happe, K., Uthes, S., Sattler, C., 2007a.Agricultural management issues of implementingmultifunctionality: commodity and non-commodityproduction in the approach of the MEA-Scope project.In: Mander, U., et al. (Eds.), Multifunctional Land

Use: Meeting Future Demands for Landscape Goodsand Services. Springer-Verlag, Berlin, pp. 167–182.

Piorr, A., Ungaro, F., Sattler, C. Damgaard, M., Osuch, A.,Happe, K., Ciancaglini, A., Uthes, S., 2007b. Summaryof results for the implementation of a targeted policymodel towards multifunctionality. Series of Reports of theFP6 Research Project MEA-Scope, vol. 14. Muncheberg,Germany. Available from: http://project.zalf.de/meascope/documents/MEA-ScopeD7.6.pdf [Accessed 15 November2008].

Rienks, W.A. (Ed.), 2008. The Future of Rural Europe. AnAnthology Based on the Results of the Eururalis 2.0.Scenario Study. Wageningen University Research andNetherlands Environmental Assessment Agency,Wageningen, The Netherlands.

Sattler, C., 2008. Okologische Bewertung und Akzeptanzanalysepflanzenbaulicher Produktionsverfahren. PhD thesis.Humboldt-Universitat, Berlin.

Sattler, C., Schuler, J., Zander, P., 2006. Determination oftrade-off-functions to analyse the provision ofagricultural non-commodities. International Journal ofAgricultural Resources, Governance and Ecology 5 (2/3),309–325.

Stremme, H., 1951. Bodenkarte der DDR. In: Bodenkunde undBodenkultur. Heft 1, Dresden.

Swinbank, A., Tangermann, S., 2001. The future of directpayments under the cap: a proposal. Eurochoices 1 (1),28–29 and 32–25. Available from: http://dx.doi.org/10.1111/j.1746-692X.2001.tb00071.x [Accessed 18 November 2008].

Tscherning, K., Konig, H., Schoßer, B., Helming, K., Sieber, S.,2008. Ex-ante impact assessment (IA) in the EuropeanCommission—an overview. In: Helming, K., Perez-Soba, M.,Tabbush, P. (Eds.), Sustainability Impact Assessmentof Land Use Changes. Springer-Verlag, Berlin,pp. 17–33.

Ungaro, F., Ciancaglini, A., Sattler, C., Uthes, S., Damgaard, M.,Happe, K., Osuch, A., 2008. Spatial characteristics of landuse patterns in Mugello (Central Italy) and policy impacts ontheir environmental outputs. In: Piorr, A., Muller, K. (Eds.)Impact of Agricultural Policies in a Europe of Change.Springer, Berlin, in press.

Ungaro, F., Venuti, L., Ciancaglini, A., 2006. Upscaling Approachfor the Mugello Area. I: Soil Quality. Instituto di Ricerca perla Protenzione Idrogeologica, CNR–IRPI Firenze, Italy,pp. 1–8.

Uthes, S., Sattler, C., Reinhardt, F.-J., Piorr, A., Zander, P., Happe,K., Damgaard, M., Osuch, A., 2008. Ecological effectsof payment decoupling in a case study region inGermany. Journal of Farm Management 13 (3),219–230.

Verburg, P.H., Kolk, K., Gilmore R.P.Jr., Veldkamp, A., 2006.Modeling land-use and land-cover change. In: Lambin, E.F.,Geist, H.J. (Eds.), Land-Use and Land-Cover Change. LocalProcesses and Global Impacts. Springer-Verlag, Berlin, pp.117–135.

Waarts, Y., 2007. Indicators for agricultural policy impactassessment in the case of beef production. In: Mander, U.,et al. (Eds.), Multifunctional Land Use: Meeting FutureDemands for Landscape Goods and Services. Springer-Verlag, Berlin, pp. 281–291.

Zander, P., Kachele, H., 1999. Modelling multiple objectives ofland use for sustainable development. Agricultural Systems59, 311–325.



Annette Piorr is a senior researcher and head of research groups atZALF. She holds a PhD in Agronomy from the University of Bonn.Her scientific expertise covers sustainable land use, multifunc-tional agriculture, organic farming, indicator development, impactassessment and evaluation, including policy makers and stake-holder involvement. She carried out the mid-term evaluation ofthe Rural Development Programme in Brandenburg/Berlin andrecent research on the CAP 2003 reform. She co-ordinated theFP6 EU-research project MEA-Scope.

Fabrizio Ungaro is a research scientist who graduated in TropicalAgriculture with a PhD in Soil Science. He is a specialist in geospatialmodelling, applied environmental soil physics and pedometricswith long years experience in the national and international con-text. He is senior researcher at the CNR IRPI in Florence.

Arianna Ciancaglini holds a Bachelor degree in AgriculturalScience from the University of Italy. From 2006 to 2007 she colla-borated with the Department of Agricultural and Land Economy,University of Florence involved in the MEA-Scope project. She isPhD student in Rural Development at the University of Florence.

Kathrin Happe holds a PhD in agricultural economics and is work-ing on structural change and policy analysis. Currently, she is asenior researcher at the Leibniz Institute of Agricultural Develop-ment in Central and Eastern Europe in Halle (Saale), Germany.

Amanda Sahrbacher has studied Agricultural and LifeSciences and Engineering at INA P-G in Paris (now AgroParis-Tech). She got her MS in Environmental Economics there andjoined the IAMO in 2004, where she was involved in the 6thFramework EU project MEA-Scope. She is currently studyingfor her PhD on distributive impacts of CAP using an agent-basedapproach.

Claudia Sattler is a scientist at the ZALF Institute of Socio-Eco-nomics. Her research deals with the ecological assessment ofcropping practices and modelling as well as the acceptance byfarmers of the implementation of more environmental friendlycrop management and land use practices.

Sandra Uthes is an agricultural economist at ZALF. Her mainresearch interests are in policies for sustainable and multifunc-tional agricultural land use, whole farm modelling, and trade-offanalyses between economic and ecological objectives of agricul-tural land use practices.

Peter Zander holds a PhD in Production Ecology and ResourceConservation from Wageningen University and is a Senior Scien-tist at the Institute of Socio-Economics at the Centre for Agricul-tural Landscape Research (ZALF). His research interest is themodelling of farm level decision making related to environmentalimpact of crop production systems.


integrated assessment of future cap policies: land...

Documents